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Flexible Shapelets Discovery for Time Series Classification
conference contribution
posted on 2020-01-01, 00:00 authored by Borui Cai Borui, Guangyan HuangGuangyan Huang, Maia Angelova TurkedjievaMaia Angelova Turkedjieva, Yong XiangYong Xiang, C H Chi© Springer Nature Singapore Pte Ltd 2020. Time series classification is important due to its pervasive applications, especially for the emerging Smart City applications that are driven by intelligent sensors. Shapelets are sub-sequences of time series that have highly predictive abilities, and time series represented by shapelets can better reveal the patterns thus have better classification accuracy. Finding shapelets is challenging as its computational in-feasibility, most existing methods only finds shapelets with a same length or a few fixed length shapelets because the searching space of shapelets with arbitrary length is too large. In this paper, we improve the time series classification accuracy by discovering shapelets with arbitrary lengths. We borrow the idea of Apriori algorithm in association rule learning, that is, the superset shapelet candidates of a poor predictive shapelet candidate also have poor predictive abilities. Therefore, we propose a Flexible Shapelets Discovery (FSD) algorithm to discover shapelets with varying lengths. In FSD, shapelet candidates having the lower bound of length are discovered, and then we extend them into arbitrary lengths shapelets as long as their predictive abilities increases. Experiments conducted on 6 UCR time series datasets demonstrate that the arbitrary length shapelets discovered by FSD achieves better classification accuracy than those using fixed length shapelets.
History
Event
Data Science. Conference (2019 : 6th : Ningbo, China)Volume
1179Series
Communications in Computer and Information SciencePagination
211 - 220Publisher
SpringerLocation
Ningbo, ChinaPlace of publication
Berlin, GermanyPublisher DOI
Start date
2019-05-15End date
2019-05-20ISSN
1865-0929eISSN
1865-0937ISBN-13
9789811528095Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
ICDS 2019 : Data science : 6th International Conference, ICDS 2019, Ningbo, China, May 15-20, 2019, revised selected papersUsage metrics
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